Token Limit, every LLM Developer should Know !
Tokens in Large Language Models (LLMs) like GPT-3 and PaLM2 are units of data read in one step, with token limits affecting model performance. Understanding these limits is key to optimizing your use
When it comes to Large Language Models (LLMs) like GPT-3 or PaLM2, there's an important concept we often encounter: 𝗧𝗼𝗸𝗲𝗻𝘀. One of the challenges in working with language models is the limitation imposed by the maximum number of tokens they can handle.
𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝗧𝗼𝗸𝗲𝗻𝘀 ?
A token, in the context of LLMs, is a unit of data the model reads in one step. Depending on the model's tokenization method, a token can be a single character, a word, or even a subword. Here are some helpful rules of thumb for understanding tokens in terms of lengths:
• 1 token ~= 4 chars in English
• 1 token ~= ¾ words
• 100 tokens ~= 75 words
𝗪𝗵𝗮𝘁 𝗮𝗿𝗲 𝗧𝗼𝗸𝗲𝗻 𝗟𝗶𝗺𝗶𝘁𝘀 ?
The number of tokens a model can handle at once, known as the 'token limit', is a crucial metric. This limit impacts both the length of text the model can consider and the amount of context it can use when generating responses. For instance, 𝗚𝗣𝗧-𝟯 has a token limit of 𝟮𝗞, while 𝗔𝗻𝘁𝗵𝗿𝗼𝗽𝗶𝗰'𝘀 𝗖𝗹𝗮𝘂𝗱𝗲 tops out at 𝟭𝟬𝟬𝗞.
Check out the bar chart below for a comparison of token limits across popular LLMs.
𝗪𝗵𝘆 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿 𝘁𝗼 𝘆𝗼𝘂?
Understanding token limits can help you optimize your usage of these models. For example, when you're using a model to generate text, if your input prompt is too long, the model might not have enough tokens left to provide a meaningful response. Conversely, a very short prompt might not give the model enough context to generate a useful reply.
As AI continues to evolve, so too will our understanding of these metrics and their implications. So, keep the concept of tokens in mind when you're working with LLMs - it's more important than you might think!
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